{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H08esTFOYO99"
      },
      "source": [
        "# Main imports and code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnHQoayhBYlm",
        "outputId": "67161847-d14c-445a-a868-56f165c7123e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Thu Mar  3 22:23:55 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 470.57.02    Driver Version: 470.57.02    CUDA Version: 11.4     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA GeForce ...  On   | 00000000:01:00.0  On |                  N/A |\n",
            "|  0%   49C    P8    42W / 370W |   3684MiB / 24265MiB |     24%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|    0   N/A  N/A      2610      G   /usr/lib/xorg/Xorg                102MiB |\n",
            "|    0   N/A  N/A      5583      G   /usr/lib/xorg/Xorg               1097MiB |\n",
            "|    0   N/A  N/A      5712      G   /usr/bin/gnome-shell              382MiB |\n",
            "|    0   N/A  N/A      7800      G   ...AAAAAAAAA= --shared-files      790MiB |\n",
            "|    0   N/A  N/A      8426      G   ...334855534997614631,131072     1175MiB |\n",
            "|    0   N/A  N/A     65593      G   ...AAAAAAAAA= --shared-files      116MiB |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "# check which gpu we're using\n",
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hYhFR7nSYOjG",
        "outputId": "6b128001-6a14-4e7d-84a8-1396e7745891"
      },
      "outputs": [],
      "source": [
        "# !pip install simpletransformers\n",
        "# !pip install tensorboardx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "RJC8wj73Zd_p"
      },
      "outputs": [],
      "source": [
        "from simpletransformers.classification import ClassificationModel, ClassificationArgs, MultiLabelClassificationModel, MultiLabelClassificationArgs\n",
        "from urllib import request\n",
        "import pandas as pd\n",
        "import logging\n",
        "import torch\n",
        "from collections import Counter\n",
        "from ast import literal_eval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bsX3b7ZNYVZe",
        "outputId": "a5fd2377-2bfe-4281-e38a-db317c6d8bb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cuda available?  True\n"
          ]
        }
      ],
      "source": [
        "# prepare logger\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "\n",
        "transformers_logger = logging.getLogger(\"transformers\")\n",
        "transformers_logger.setLevel(logging.WARNING)\n",
        "\n",
        "# check gpu\n",
        "cuda_available = torch.cuda.is_available()\n",
        "\n",
        "print('Cuda available? ',cuda_available)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HpRLLRzkTwdL",
        "outputId": "9529644a-2ae5-47f9-d6ac-09c5ec8fe191"
      },
      "outputs": [],
      "source": [
        "if cuda_available:\n",
        "  import tensorflow as tf\n",
        "  # Get the GPU device name.\n",
        "#   device_name = tf.test.gpu_device_name()\n",
        "#   # The device name should look like the following:\n",
        "#   if device_name == '/device:GPU:0':\n",
        "#       print('Found GPU at: {}'.format(device_name))\n",
        "#   else:\n",
        "#       raise SystemError('GPU device not found')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BMQDATlOZHxu"
      },
      "source": [
        "# Fetch Don't Patronize Me! data manager module"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UW903YxwThrH",
        "outputId": "3e11ab8f-4114-448b-8f1d-fabafc321ee9"
      },
      "outputs": [],
      "source": [
        "# module_url = f\"https://raw.githubusercontent.com/Perez-AlmendrosC/dontpatronizeme/master/semeval-2022/dont_patronize_me.py\"\n",
        "# module_name = module_url.split('/')[-1]\n",
        "# print(f'Fetching {module_url}')\n",
        "# #with open(\"file_1.txt\") as f1, open(\"file_2.txt\") as f2\n",
        "# with request.urlopen(module_url) as f, open(module_name,'w') as outf:\n",
        "#   a = f.read()\n",
        "#   outf.write(a.decode('utf-8'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "PRxm0179aqzw"
      },
      "outputs": [],
      "source": [
        "# helper function to save predictions to an output file\n",
        "def labels2file(p, outf_path):\n",
        "\twith open(outf_path,'w') as outf:\n",
        "\t\tfor pi in p:\n",
        "\t\t\toutf.write(','.join([str(k) for k in pi])+'\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "gcDThFWVBxGb"
      },
      "outputs": [],
      "source": [
        "from dont_patronize_me import DontPatronizeMe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "3Ay5_5Y0ThrI"
      },
      "outputs": [],
      "source": [
        "dpm = DontPatronizeMe('data/', 'data/task4_test.tsv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2r3USK4eThrJ",
        "outputId": "bc7745c5-caa5-4c94-ce7e-37ed00a25094"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Map of label to numerical label:\n",
            "{'Unbalanced_power_relations': 0, 'Shallow_solution': 1, 'Presupposition': 2, 'Authority_voice': 3, 'Metaphors': 4, 'Compassion': 5, 'The_poorer_the_merrier': 6}\n"
          ]
        }
      ],
      "source": [
        "dpm.load_task1()\n",
        "dpm.load_task2(return_one_hot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0YcdU80IbiS"
      },
      "source": [
        "# Load paragraph IDs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "8AReWYHYOUqx"
      },
      "outputs": [],
      "source": [
        "trids = pd.read_csv('data/train_semeval_parids-labels.csv')\n",
        "teids = pd.read_csv('data/dev_semeval_parids-labels.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a-_ADoJAOWJA",
        "outputId": "613d373b-56ed-483d-9481-c8d1d097a41e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>par_id</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4341</td>\n",
              "      <td>[1, 0, 0, 1, 0, 0, 0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4136</td>\n",
              "      <td>[0, 1, 0, 0, 0, 0, 0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10352</td>\n",
              "      <td>[1, 0, 0, 0, 0, 1, 0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8279</td>\n",
              "      <td>[0, 0, 0, 1, 0, 0, 0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1164</td>\n",
              "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   par_id                  label\n",
              "0    4341  [1, 0, 0, 1, 0, 0, 0]\n",
              "1    4136  [0, 1, 0, 0, 0, 0, 0]\n",
              "2   10352  [1, 0, 0, 0, 0, 1, 0]\n",
              "3    8279  [0, 0, 0, 1, 0, 0, 0]\n",
              "4    1164  [1, 0, 0, 1, 1, 1, 0]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trids.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "7IfCZjwQ16MS"
      },
      "outputs": [],
      "source": [
        "trids.par_id = trids.par_id.astype(str)\n",
        "teids.par_id = teids.par_id.astype(str)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8lXrNj_Ww_FC"
      },
      "source": [
        "# Rebuild training set (Task 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "def rebuild_raw_dataset(dpm, ids):\n",
        "    rows = [] # will contain par_id, label and text\n",
        "    for idx in range(len(ids)):  \n",
        "        parid = ids.par_id[idx]\n",
        "\n",
        "        # select row from original dataset\n",
        "        text = dpm.loc[dpm.par_id == parid].text.values[0]\n",
        "        label = dpm.loc[dpm.par_id == parid].label.values[0]\n",
        "        orig_label = dpm.loc[dpm.par_id == parid].orig_label.values[0]\n",
        "        art_id = dpm.loc[dpm.par_id == parid].art_id.values[0]\n",
        "        keyword = dpm.loc[dpm.par_id == parid].keyword.values[0]\n",
        "        country = dpm.loc[dpm.par_id == parid].country.values[0]\n",
        "        rows.append({\n",
        "            'par_id':parid,\n",
        "            'text':text,\n",
        "            'label':label,\n",
        "            'orig_label': orig_label,\n",
        "            'art_id': art_id,\n",
        "            'keyword': keyword,\n",
        "            'country': country\n",
        "        })\n",
        "    return pd.DataFrame(rows)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "8e3E08Yown5p"
      },
      "outputs": [],
      "source": [
        "trdf1 = rebuild_raw_dataset(dpm.train_task1_df, trids)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1KGYmpnxDjt"
      },
      "source": [
        "# Rebuild test set (Task 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "T6FLgB6KxGI2"
      },
      "outputs": [],
      "source": [
        "# rows = [] # will contain par_id, label and text\n",
        "# for idx in range(len(teids)):  \n",
        "#   parid = teids.par_id[idx]\n",
        "#   #print(parid)\n",
        "#   # select row from original dataset\n",
        "#   text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
        "#   label = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].label.values[0]\n",
        "#   rows.append({\n",
        "#       'par_id':parid,\n",
        "#       'text':text,\n",
        "#       'label':label\n",
        "#   })\n",
        "\n",
        "tedf1 = rebuild_raw_dataset(dpm.train_task1_df, teids)\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YbB9GdzJxRAH",
        "outputId": "fcd2b8f3-3881-4ca5-da31-4a836b95a68b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2094"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(tedf1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "vhBhTRIyxSaQ"
      },
      "outputs": [],
      "source": [
        "# tedf1 = pd.DataFrame(rows)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xK6FY70KZ6TY"
      },
      "source": [
        "# RoBERTa Baseline for Task 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Z-pvjbu_8h1n"
      },
      "outputs": [],
      "source": [
        "# downsample negative instances\n",
        "pcldf = trdf1[trdf1.label==1]\n",
        "npos = len(pcldf)\n",
        "\n",
        "training_set1 = pd.concat([pcldf,trdf1[trdf1.label==0][:npos*2]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "mpSqMp3d8iYu",
        "outputId": "da4151a2-50b4-4550-f862-6cfe3df19bf1"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>par_id</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "      <th>orig_label</th>\n",
              "      <th>art_id</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4341</td>\n",
              "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
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              "      <td>4</td>\n",
              "      <td>@@17139403</td>\n",
              "      <td>poor-families</td>\n",
              "      <td>gb</td>\n",
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              "      <th>1</th>\n",
              "      <td>4136</td>\n",
              "      <td>Durban 's homeless communities reconciliation ...</td>\n",
              "      <td>1</td>\n",
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              "      <td>vulnerable</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1164</td>\n",
              "      <td>To strengthen child-sensitive social protectio...</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>@@14727121</td>\n",
              "      <td>poor-families</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2378</th>\n",
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              "      <td>Then , taking the art of counter-intuitive non...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>@@7919587</td>\n",
              "      <td>refugee</td>\n",
              "      <td>ie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2379</th>\n",
              "      <td>1777</td>\n",
              "      <td>Kagunga village was reported to lack necessary...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>@@1921089</td>\n",
              "      <td>refugee</td>\n",
              "      <td>tz</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2380</th>\n",
              "      <td>1778</td>\n",
              "      <td>\"After her parents high-profile divorce after ...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>@@19805601</td>\n",
              "      <td>vulnerable</td>\n",
              "      <td>my</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2381</th>\n",
              "      <td>1779</td>\n",
              "      <td>\"Last night One News reported on leaked Minist...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>@@68600</td>\n",
              "      <td>in-need</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2382 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     par_id                                               text  label  \\\n",
              "0      4341  The scheme saw an estimated 150,000 children f...      1   \n",
              "1      4136  Durban 's homeless communities reconciliation ...      1   \n",
              "2     10352  The next immediate problem that cropped up was...      1   \n",
              "3      8279  Far more important than the implications for t...      1   \n",
              "4      1164  To strengthen child-sensitive social protectio...      1   \n",
              "...     ...                                                ...    ...   \n",
              "2377   1775  Last but not the least element of culpability ...      0   \n",
              "2378   1776  Then , taking the art of counter-intuitive non...      0   \n",
              "2379   1777  Kagunga village was reported to lack necessary...      0   \n",
              "2380   1778  \"After her parents high-profile divorce after ...      0   \n",
              "2381   1779  \"Last night One News reported on leaked Minist...      0   \n",
              "\n",
              "     orig_label      art_id        keyword country  \n",
              "0             4  @@17139403  poor-families      gb  \n",
              "1             2  @@22273328       homeless      za  \n",
              "2             4  @@21102155  poor-families      lk  \n",
              "3             2  @@21220476     vulnerable      nz  \n",
              "4             4  @@14727121  poor-families      gh  \n",
              "...         ...         ...            ...     ...  \n",
              "2377          0   @@9622478        refugee      nz  \n",
              "2378          0   @@7919587        refugee      ie  \n",
              "2379          0   @@1921089        refugee      tz  \n",
              "2380          1  @@19805601     vulnerable      my  \n",
              "2381          0     @@68600        in-need      nz  \n",
              "\n",
              "[2382 rows x 7 columns]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "training_set1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [],
      "source": [
        "import random, os\n",
        "import numpy as np\n",
        "def seed_everything(seed: int):\n",
        "    random.seed(seed)\n",
        "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = True"
      ]
    },
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        },
        "id": "PoW_s23AZ_DG",
        "outputId": "fe45d9a8-bcef-4be0-f2f9-aa4701e7d0b5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.bias', 'lm_head.bias', 'roberta.pooler.dense.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/home/ycniu/anaconda3/envs/ic-nlp/lib/python3.8/site-packages/simpletransformers/classification/classification_model.py:585: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  warnings.warn(\n",
            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
            "  0%|          | 5/2382 [00:00<03:50, 10.32it/s]\n",
            "/home/ycniu/anaconda3/envs/ic-nlp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "Epochs 0/1. Running Loss:    0.1589: 100%|██████████| 298/298 [00:32<00:00,  9.30it/s]\n",
            "Epoch 1 of 1: 100%|██████████| 1/1 [00:32<00:00, 32.05s/it]\n",
            "INFO:simpletransformers.classification.classification_model: Training of roberta model complete. Saved to outputs/.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(298, 0.511156686450411)"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seed_everything(42)\n",
        "\n",
        "task1_model_args = ClassificationArgs(num_train_epochs=1, \n",
        "                                      no_save=True, \n",
        "                                      no_cache=True, \n",
        "                                      overwrite_output_dir=True)\n",
        "task1_model = ClassificationModel(\"roberta\", \n",
        "                                  'roberta-base', \n",
        "                                  args = task1_model_args, \n",
        "                                  num_labels=2, \n",
        "                                  use_cuda=cuda_available)\n",
        "# train model\n",
        "task1_model.train_model(training_set1[['text', 'label']])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1012\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 3/1012 [00:00<01:25, 11.78it/s]\n",
            "100%|██████████| 127/127 [00:02<00:00, 47.16it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Counter({0: 874, 1: 138})\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(0.47706422018348627, 0.37681159420289856, 0.65)"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# analysis\n",
        "analysis_dpm = DontPatronizeMe('data/', 'data/task4_test.tsv')\n",
        "analysis_dpm.load_task1(\"test_short.tsv\")\n",
        "analysis = analysis_dpm.train_task1_df\n",
        "print(len(analysis))\n",
        "\n",
        "preds_task1, _ = task1_model.predict(analysis.text.tolist())\n",
        "\n",
        "print(Counter(preds_task1))\n",
        "from datasets import load_metric\n",
        "\n",
        "metric_f1 = load_metric(\"f1\")\n",
        "metric_precision = load_metric(\"precision\")\n",
        "metric_recall = load_metric(\"recall\")\n",
        "\n",
        "\n",
        "f1 = metric_f1.compute(predictions=preds_task1, references=analysis.label.tolist())[\"f1\"]\n",
        "precision = metric_precision.compute(predictions=preds_task1, references=analysis.label.tolist())[\"precision\"]\n",
        "recall = metric_recall.compute(predictions=preds_task1, references=analysis.label.tolist())[\"recall\"]\n",
        "f1, precision, recall"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "kzupJuwTafKa"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n",
            "  0%|          | 8/3832 [00:00<07:24,  8.60it/s]\n",
            "100%|██████████| 479/479 [00:10<00:00, 47.44it/s]\n"
          ]
        }
      ],
      "source": [
        "dpm.load_test()\n",
        "preds_task1, _ = task1_model.predict(dpm.test_set_df.text.tolist())\n",
        "labels2file([[k] for k in preds_task1], 'task1.txt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7Cc_u5Oli7j"
      },
      "source": [
        "# Rebuild training set (Task 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D2WLYT7wli7k"
      },
      "outputs": [],
      "source": [
        "rows2 = [] # will contain par_id, label and text\n",
        "for idx in range(len(trids)):  \n",
        "  parid = trids.par_id[idx]\n",
        "  label = trids.label[idx]\n",
        "  # select row from original dataset to retrieve the `text` value\n",
        "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
        "  rows2.append({\n",
        "      'par_id':parid,\n",
        "      'text':text,\n",
        "      'label':label\n",
        "  })\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LFqMMb5Jli7l"
      },
      "outputs": [],
      "source": [
        "trdf2 = pd.DataFrame(rows2)\n",
        "trdf2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MxHLB_g0pfEb"
      },
      "outputs": [],
      "source": [
        "trdf2.label = trdf2.label.apply(literal_eval)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gukbmv0bli7l"
      },
      "source": [
        "# Rebuild test set (Task 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gjH-AJK1li7m"
      },
      "outputs": [],
      "source": [
        "rows2 = [] # will contain par_id, label and text\n",
        "for idx in range(len(teids)):  \n",
        "  parid = teids.par_id[idx]\n",
        "  label = teids.label[idx]\n",
        "  #print(parid)\n",
        "  # select row from original dataset to access the `text` value\n",
        "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
        "  rows2.append({\n",
        "      'par_id':parid,\n",
        "      'text':text,\n",
        "      'label':label\n",
        "  })\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SRP-tn5wli7n"
      },
      "outputs": [],
      "source": [
        "tedf2 = pd.DataFrame(rows2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "8U2lrfJiolku",
        "outputId": "6bf1181c-3e95-4913-cceb-9cc9e08b6c29"
      },
      "outputs": [],
      "source": [
        "tedf2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "81aQFjWqpbe2"
      },
      "outputs": [],
      "source": [
        "tedf2.label = tedf2.label.apply(literal_eval)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKFiVaslbAiC"
      },
      "source": [
        "# RoBERTa baseline for Task 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hmr5ZZf5Ik5T"
      },
      "outputs": [],
      "source": [
        "all_negs = trdf2[trdf2.label.apply(lambda x:sum(x) == 0)]\n",
        "all_pos = trdf2[trdf2.label.apply(lambda x:sum(x) > 0)]\n",
        "\n",
        "training_set2 = pd.concat([all_pos,all_negs[:round(len(all_pos)*0.5)]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "zyBcJoHtJHE2",
        "outputId": "983b27a6-3bec-47bc-e564-79face4b061c"
      },
      "outputs": [],
      "source": [
        "training_set2"
      ]
    },
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        "colab": {
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        },
        "id": "ECb7_mwzbFa6",
        "outputId": "d433e1aa-c3c4-4a00-9b2a-c04ccdc20588"
      },
      "outputs": [],
      "source": [
        "task2_model_args = MultiLabelClassificationArgs(num_train_epochs=1,\n",
        "                                                no_save=True, \n",
        "                                                no_cache=True, \n",
        "                                                overwrite_output_dir=True\n",
        "                                                )\n",
        "task2_model = MultiLabelClassificationModel(\"roberta\", \n",
        "                                            'roberta-base', \n",
        "                                            num_labels=7,\n",
        "                                            args = task2_model_args, \n",
        "                                            use_cuda=cuda_available)\n",
        "# train model\n",
        "task2_model.train_model(training_set2[['text', 'label']])\n",
        "# run predictions\n",
        "preds_task2, _ = task2_model.predict(tedf2.text.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0211sxhsbbWZ"
      },
      "outputs": [],
      "source": [
        "labels2file(preds_task2, 'task2.txt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RT8hjnxbbfJq"
      },
      "source": [
        "## Prepare submission"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7HICl8MJQf0",
        "outputId": "794d8101-dd24-4dd5-b315-93c931a6b040"
      },
      "outputs": [],
      "source": [
        "!cat task1.txt | head -n 10"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qCjziGtxJRif",
        "outputId": "aef99217-9b4d-46f7-f3b9-c9313bc165dc"
      },
      "outputs": [],
      "source": [
        "!cat task2.txt | head -n 10"
      ]
    },
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      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZDLUcYZbhYg",
        "outputId": "7586017d-83f2-4665-eb44-e2264201ac30"
      },
      "outputs": [],
      "source": [
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